**Holo3.1** is our latest family of Vision-Language Models (VLMs) for computer use agents. Building on Holo3, it expands support beyond browser and desktop automation to mobile environments, introduces native function-calling support for seamless integration with agent frameworks, and enables local deployment through optimized quantized checkpoints. The Holo3.1 family spans model sizes from 0.8B to 35B-A3B parameters. Across computer use, UI grounding, mobile automation, and business workflows, Holo3.1 delivers strong performance while improving deployment flexibility and cost efficiency. * **Developed by:** **H Company** * **Model type:** Vision-Language Models for Navigation and Computer Use Agents * **Available models:** Holo3.1-0.8B, Holo3.1-4B, Holo3.1-9B, Holo3.1-35B-A3B * **Base models:** Qwen 3.5 family * **Supported environments:** Web, Desktop, Mobile * **Available quantizations for Holo3.1-35B-A3B:** BF16, FP8, NVFP4, Q4 GGUF * **Blog Post:** hcompany.ai/holo3.1 * **Quickstart:** hub.hcompany.ai/quickstart * **License:** Apache 2.0 License The figure below compares the overall performance and inference cost of the Holo3.1 and Qwen 3.5 families. Overall performance averages computer use, mobile automation, enterprise workflows, and UI grounding benchmarks.
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**Holo3.1** is our latest family of Vision-Language Models (VLMs) for computer use agents. Building on Holo3, it expands support beyond browser and desktop automation to mobile environments, introduces native function-calling support for seamless integration with agent frameworks, and enables local deployment through optimized quantized checkpoints. The Holo3.1 family spans model sizes from 0.8B to 35B-A3B parameters. Across computer use, UI grounding, mobile automation, and business workflows, Holo3.1 delivers strong performance while improving deployment flexibility and cost efficiency. * **Developed by:** **H Company** * **Model type:** Vision-Language Models for Navigation and Computer Use Agents * **Available models:** Holo3.1-0.8B, Holo3.1-4B, Holo3.1-9B, Holo3.1-35B-A3B * **Base models:** Qwen 3.5 family * **Supported environments:** Web, Desktop, Mobile * **Available quantizations for Holo3.1-35B-A3B:** BF16, FP8, NVFP4, Q4 GGUF * **Blog Post:** hcompany.ai/holo3.1 * **Quickstart:** hub.hcompany.ai/quickstart * **License:** Apache 2.0 License The figure below compares the overall performance and inference cost of the Holo3.1 and Qwen 3.5 families. Overall performance averages computer use, mobile automation, enterprise workflows, and UI grounding benchmarks.
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**Holo2** represents the next major step in developing large-scale Vision-Language Models (VLMs) for **multi-domain GUI Agents**. These agents can operate real digital environments specifically web, desktop, and mobile by interpreting interfaces, reasoning over content, and executing actions. Our **Holo2** family emphasizes **navigation and task execution** across diverse real and simulated environments, extending beyond static perception to **multi-step, goal-directed behavior**. It builds upon the strengths of **Holo1.5** in UI localization and screen content understanding, with major improvements in **policy learning**, **action grounding**, and **cross-environment generalization**.
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> [!IMPORTANT] > *Holo4* family: > > - Holo4-27B: Qwen3.8 dense architecture. BF16, FP8, NVFP4, and Q4 GGUF [current]. > - Holo4-35B-A3B: Qwen3.6 MoE architecture. BF16, FP8, NVFP4, and Q4 GGUF. > - Holotron4-30B-A3B: NemotronH Nano Omni architecture. BF16 and FP8. *Holo4*-27B-GGUF is a vision-language model (VLM) for Computer Use, built on Qwen3.8-27B and developed by H Company. Used with the hai-agents harness, it can send screenshots and tool results to the model, then execute its requested clicks, typing, code, and tool calls. Parameters 27B dense Maximum context length in config 262,144 tokens Model type Visual Language Model (VLM) Architecture Qwen3.8 dense Interfaces Graphical interface, code, and tool calls Target environments Web, desktop, and mobile
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> [!IMPORTANT] > *Holo4* family: > > - Holo4-27B: Qwen3.8 dense architecture. BF16, FP8, NVFP4, and Q4 GGUF. > - Holo4-35B-A3B: Qwen3.6 MoE architecture. BF16, FP8, NVFP4, and Q4 GGUF [current]. > - Holotron4-30B-A3B: NemotronH Nano Omni architecture. BF16 and FP8. *Holo4*-35B-A3B-GGUF is a vision-language model (VLM) for Computer Use, built on Qwen3.6-35B-A3B and developed by H Company. Used with the hai-agents harness, it can send screenshots and tool results to the model, then execute its requested clicks, typing, code, and tool calls. Model ID Hcompany/Holo4-35B-A3B-GGUF Architecture Qwen3.6 MoE Checkpoint format Q4_K_M GGUF Maximum context length in config 262,144 tokens
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NeoMME is an efficient Multilingual and Multimodal-native foundational Encoder. Text tokens and raw image patches pass through one shared Transformer encoder. NeoMME does not use a separately pretrained vision encoder or a causal language model. NeoMME-260M is a pretrained encoder backbone and cannot be used on its own for a downstream task. It returns contextual token representations, so users should fine-tune a task-specific head for retrieval, classification, extraction, or another downstream task. For document retrieval, use NeoMME-260M-Retriever. Parameters 263M Vocabulary 131,072 tokens Context length 16,384 tokens Hidden size 1,024 Image patches 32 × 32 pixels, up to 2,048 pixels on the longest side (default)
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